Zhe Li

Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention

Handwritten mathematical expression recognition (HMER) is an important r...
07/20/2020 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Joint Multi-Dimension Pruning

We present joint multi-dimension pruning (named as JointPruning), a new ...
05/18/2020 ∙ by Zechun Liu, et al. ∙ 6 ∙ share

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Robust 3D Self-portraits in Seconds

In this paper, we propose an efficient method for robust 3D self-portrai...
04/06/2020 ∙ by Zhe Li, et al. ∙ 8 ∙ share

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PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries

Range aggregate queries find frequent application in data analytics. In ...
03/18/2020 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

Outlier detection is an important task in data mining and many technolog...
03/07/2020 ∙ by Zhe Li, et al. ∙ 11 ∙ share

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Long Short-Term Sample Distillation

In the past decade, there has been substantial progress at training incr...
03/02/2020 ∙ by Liang Jiang, et al. ∙ 5 ∙ share

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Learning From Brains How to Regularize Machines

Despite impressive performance on numerous visual tasks, Convolutional N...
11/11/2019 ∙ by Zhe Li, et al. ∙ 36 ∙ share

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Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation

Accurate multi-organ abdominal CT segmentation is essential to many clin...
04/12/2019 ∙ by Yuyin Zhou, et al. ∙ 0 ∙ share

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CircConv: A Structured Convolution with Low Complexity

Deep neural networks (DNNs), especially deep convolutional neural networ...
02/28/2019 ∙ by Siyu Liao, et al. ∙ 0 ∙ share

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E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...
12/12/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Generative Adversarial Active Learning for Unsupervised Outlier Detection

Outlier detection is an important topic in machine learning and has been...
09/28/2018 ∙ by Yezheng Liu, et al. ∙ 18 ∙ share

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A Unified Analysis of Stochastic Momentum Methods for Deep Learning

Stochastic momentum methods have been widely adopted in training deep ne...
08/30/2018 ∙ by Yan Yan, et al. ∙ 0 ∙ share

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EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching

Designing the structure of neural networks is considered one of the most...
06/05/2018 ∙ by Jian Ren, et al. ∙ 0 ∙ share

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An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

Recently, there emerged revived interests of designing automatic program...
06/03/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing

Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendo...
05/10/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Learning Topics using Semantic Locality

The topic modeling discovers the latent topic probability of the given t...
04/11/2018 ∙ by Ziyi Zhao, et al. ∙ 0 ∙ share

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Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...
03/20/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

Increasing malicious users have sought practices to leverage 3D printing...
03/20/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

Recently, significant accuracy improvement has been achieved for acousti...
03/14/2018 ∙ by Shuo Wang, et al. ∙ 0 ∙ share

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A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

Customer retention is one of the primary goals in the area of customer r...
02/25/2018 ∙ by Kun Hu, et al. ∙ 0 ∙ share

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Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

Hardware accelerations of deep learning systems have been extensively in...
02/18/2018 ∙ by Yanzhi Wang, et al. ∙ 0 ∙ share

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Image Dataset for Visual Objects Classification in 3D Printing

The rapid development in additive manufacturing (AM), also known as 3D p...
02/15/2018 ∙ by Hongjia Li, et al. ∙ 0 ∙ share

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An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing

With recent trend of wearable devices and Internet of Things (IoTs), it ...
02/03/2018 ∙ by Xiaolong Ma, et al. ∙ 0 ∙ share

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Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

Automatic diagnosing lung cancer from Computed Tomography (CT) scans inv...
11/22/2017 ∙ by Fangzhou Liao, et al. ∙ 0 ∙ share

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Thoracic Disease Identification and Localization with Limited Supervision

Accurate identification and localization of abnormalities from radiology...
11/17/2017 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

In this paper, we present a simple analysis of fast rates with high pr...
09/09/2017 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

Large-scale deep neural networks (DNNs) are both compute and memory inte...
08/29/2017 ∙ by Caiwen Ding, et al. ∙ 0 ∙ share

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SEP-Nets: Small and Effective Pattern Networks

While going deeper has been witnessed to improve the performance of conv...
06/13/2017 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks

Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecede...
03/12/2017 ∙ by Ji Li, et al. ∙ 0 ∙ share

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Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

Recently low displacement rank (LDR) matrices, or so-called structured m...
03/01/2017 ∙ by Liang Zhao, et al. ∙ 0 ∙ share

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SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

With recent advancing of Internet of Things (IoTs), it becomes very attr...
11/18/2016 ∙ by Ao Ren, et al. ∙ 0 ∙ share

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Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization

Recently, stochastic momentum methods have been widely adopted in train...
04/12/2016 ∙ by Tianbao Yang, et al. ∙ 0 ∙ share

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Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training deep neural ne...
02/06/2016 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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Zhe Li

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